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"description": ".. image:: https://raw.githubusercontent.com/koelling/amplimap/master/amplimap_logo_400px.png\n\t:width: 400px\n\n\n.. image:: https://circleci.com/gh/koelling/amplimap.svg?style=svg\n :target: https://circleci.com/gh/koelling/amplimap\n.. image:: https://readthedocs.org/projects/amplimap/badge/?version=latest\n :target: https://amplimap.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n\n==========================================================\namplimap: amplicon mapping and analysis pipeline\n==========================================================\n\namplimap is a command-line tool to automate the processing and analysis of data from targeted next-generation sequencing (NGS) experiments with PCR-based amplicons or capture-based enrichment systems.\n\nFrom raw sequencing reads, amplimap generates a variety of output files including read alignments, per-basepair nucleotide counts, target coverage data and annotated variant calls.\n\nIn addition to its focus on user-friendliness and reproducibility, amplimap supports advanced features such as the generation of consensus base calls for read families based on molecular identifiers/barcodes (UMIs) and the detection of chimeric reads caused by amplification of off-target loci.\n\nOverview\n----------\nTo run amplimap you create a directory containing a small set of input files:\n\n- A subdirectory with FASTQ.GZ or BAM files representing your different samples (tested with Illumina MiSeq, HiSeq and NextSeq)\n\n- Optionally: Files describing the targeted genomic regions, the primers you used or other custom configuration parameters\n\nThen you can run ``amplimap`` to generate a variety of different output files, depending on your experiment.\nThese include, for example:\n\n1. A target coverage table, showing you how well-covered each target region was in each sample.\n\n2. A table of germline variants in your samples, annotated with gene, impact, population frequencies, deleteriousness scores, etc.\n\n3. A per-basepair \u201cpileup\u201d table telling you how often each nucleotide was seen in each sample at each position.\n\nBuilt on top of `Snakemake `_ and Python 3, amplimap is entirely\nautomated and can be run on a single machine as well as on an HPC cluster\n(e.g. LSF, SGE).\n\nSupported experimental protocols\n---------------------------------\namplimap is compatible with most targeted sequencing protocols that generate paired-end short read data.\n\nFor protocols utilising PCR or smMIPs each read should start with a known primer (or targeting arm) sequence, followed by the amplified target DNA.\nReads can optionally contain a unique molecular identifier (UMI) sequence in front of the primer, which can be used to group reads into families.\nData should be available as demultiplexed FASTQ.GZ files, with each pair of files representing a different sample.\n\nFor capture-based protocols data can be provided in FASTQ.GZ or unmapped/mapped BAM format, which may contain UMIs as BAM tags.\n\nSome of the protocols we have analyzed with amplimap include:\n\n- PCR-based targeted resequencing (single/multiplex)\n- `smMIPs with and without UMIs `_\n- Probe based target enrichment, for example:\n\n - `IDT xGen Lockdown probes `_\n - `Twist Bioscience Custom Panels `_\n\nTutorials\n---------\n\n- `Calling germline variants in amplicon-based resequencing data `_\n- `Identifying low-frequency somatic mutations in FGFR2 with UMI-tagged smMIPs `_\n- `Quantifying allele-specific expression `_\n\nLinks\n--------\n \n- Package: https://pypi.org/project/amplimap/\n- Code: https://github.com/koelling/amplimap/\n- Documentation: https://amplimap.readthedocs.io/\n\n\nInstallation\n-------------------\nWe recommend that you install amplimap through Conda:\n\n::\n\n wget https://raw.githubusercontent.com/koelling/amplimap/master/environment.yml\n conda env create --file environment.yml\n\nWe also have a `Docker image `_ available.\nPlease see our\n`full installation instructions `_\nfor additional details.\n\n\nCitation and License\n--------------------\nLicensed under the Apache License, version 2.0.\nCopyright 2019 Nils Koelling.\nWhen you use amplimap, please cite it in your work. For example:\n\n Koelling, Nils. \"amplimap: amplicon mapping and analysis pipeline\". https://github.com/koelling/amplimap/",
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